Higher education % in Oregon - Alis volat propriis
Welcome to my notebook covering the use of the tidycensus R package for extracting and evaluating census data in the state of Oregon. We are interested in uncovering the percentages of the population with graduate level degrees at a county level.
Start by importing the list of libraries that will assist us in connecting with the Census Bureau’s APIs and visualizing the desired data.
library(tidycensus) # connect with the census bureau API
library(tidyverse) # data wrangling
library(mapview) # spatial data visualizer
library(plotly) # graph / chart visualizer
library(ggiraph) # graph / chart visualizer
library(ggplot2)
library(DT)
library(scales)
library(sf)We will use the get_acs() function from tidycensus package to pull data from the census bureau. The function defaults to the 2017-2021 5-year ACS dataset. Which is necessary for us, since there are a list of counties in Oregon with a population of less than 65,000.
# Data objects needed
or_grads <- get_acs(
geography = "county",
variables = "DP02_0066P",
state = "OR",
year = 2021,
)
or_grads_table <- or_grads %>%
separate(NAME, into = c("county", "state"), sep = ", ") %>%
arrange(-estimate) %>%
select(county, estimate, moe)We have our results in a table and we can filter through to see all counties with their percent of population with graduate degrees. Lets use the datatable() function from the DT library to provide a bit more interactivity for previewing the results.
# function from the DT library
datatable(or_grads_table)Top 3 counties –
- Benton
- Multnomah
- Washington
Bottom 3 counties –
- Malheur
- Gilliam
- Morrow
or_plot <- ggplot(or_grads_table, aes(x = estimate,
y = reorder(county, estimate))) +
geom_point(color="navy", size = 3) +
scale_x_continuous(labels = function(x) paste0(x, '%')) +
scale_y_discrete(labels = function(x) str_remove(x, " County")) +
labs(title = "% Population with graduate degrees, 2017-2021 ACS",
subtitle = "Counties in Oregon",
caption = "Data acquired with R and tidycensus",
x = "ACS estimate",
y = "") +
theme_minimal(base_size = 12)
or_plot
or_plot_errorbar <- ggplot(or_grads, aes(x = estimate, y = reorder(NAME, estimate))) +
geom_errorbar(aes(xmin = estimate - moe, xmax = estimate + moe),
width = 0.5, linewidth = 0.5) +
geom_point(color = "gold", size = 3) +
scale_x_continuous(labels = function(x) paste0(x, '%')) +
scale_y_discrete(labels = function(x) str_remove(x, " County, Oregon|, Oregon")) +
labs(title = "% Population with graduate degrees, 2017-2021 ACS",
subtitle = "Counties in Oregon",
caption = "Data acquired with R and tidycensus. Error bars represent margin of error around estimates.",
"ACS estimate",
y = "",
x = "Percentage") +
theme_minimal(base_size = 12)
or_plot_errorbar
or_income <- get_acs(
geography = "county",
variables = "B19013A_001",
state = "OR",
year = 2021,
geometry = TRUE
)##
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|======================================================================| 100%or_house <- get_acs(
geography = "county",
variables = "B25105_001",
state = "OR",
year = 2021,
geometry = TRUE
)## Getting data from the 2017-2021 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.ggplotly(or_plot_errorbar, tooltip = "x")# Data objects needed
or_grads_geom <- get_acs(
geography = "county",
variables = "DP02_0066P",
state = "OR",
year = 2021,
geometry = TRUE
)
ggplot() + # use ggplot to create the plot.
geom_sf(data = or_grads_geom, # use geom_sf to plot the polyline geometry.
aes(fill = as.factor(estimate)),
linewidth = 0.4) + # color based on year.
scale_color_discrete(name = "estimate") + # set color scale
coord_sf(datum = NA) + # put geometries on the same coordinate reference system
theme_void()
# median salary for computer programmers across the country
#income_variables <- "B24121_067"
# populatiion for computer programmers
#pop_variables <- "B24114_067"
renter_bachelors<- get_acs(
geography = "tract",
variables = "B25013_011",
state = "OR",
year = 2021,
geometry = TRUE
)## Getting data from the 2017-2021 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.##
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Downloading: 1.2 MBView(renter_bachelors)# figure out decennial data, and join total population against renter occupied housing number, find % across the country
#doctorates<- get_decennial(
# geography = "block",
# variables = "B15003_025",
# year = 2020,
# geometry = TRUE
#)
#View(doctorates)options(tigris_use_cache = TRUE)
#B25013_011
#Estimate!!Total:!!Renter-occupied housing units:!!Bachelor's degree or higher
#TENURE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER
#or <- get_decennial(geography = "tract", variables = "B25013_011", year = 2020, state = "OR", geometry = TRUE)
#mutate(pct = 100 * (value / summary_value))
#ggplot(or, aes(fill = pct, color = pct)) +
# geom_sf()
#facet_wrap(~variable)